Mass-Scale, User-Independent, Device-Independent Voice Messaging System

ABSTRACT

A mass-scale, user-independent, device-independent, voice messaging system that converts unstructured voice messages into text for display on a screen is disclosed. The system comprises (i) computer implemented sub-systems and also (ii) a network connection to human operators providing transcription and quality control; the system being adapted to optimise the effectiveness of the human operators by further comprising 3 core sub-systems, namely (i) a pre-processing front end that determines an appropriate conversion strategy; (ii) one or more conversion resources; and (iii) a quality control sub-system.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. application Ser. No.11/673,746, filed Feb. 12, 2007, which is based on and claims priorityto Great Britain Application No. GB 0602682.7, filed Feb. 10, 2006,Great Britain Application No. GB 0700376.7, filed Jan. 9, 2007, andGreat Britain Application No. GB0700377.5, filed Jan. 9, 2007, thecontents of which are fully incorporated herein by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention relates to a mass-scale, user-independent,device-independent, voice messaging system that converts unstructuredvoice messages into text for display on a screen. It is worthwhileinitially looking at the challenges facing a mass-scale,user-independent, voice messaging system that can convert unstructuredvoice messages into text. First, ‘mass-scale’—means that the systemshould be scalable to very large numbers, for example 500,000+subscribers (typically these are subscribers to a mobile telephoneoperator) and still allow effective and fast processing times—a messageis generally only useful if received within 2-5 minutes of being left.This is far more demanding than most ASR implementations. Second,‘user-independent’: this means that there is absolutely no need for auser to train the system to recognise its voice or speech patterns(unlike conventional voice dictation systems). Third,‘device-independent’: this means that the system is not tied toreceiving inputs from a particular input device; some prior art systemsrequire input from say a touch tone telephone. Fourth, ‘unstructured’:this means that messages have no pre-defined structure, unlike responseto voice prompts. Fifth, ‘voice messages’: this is a very specific andquite narrow application field that raises different challenges to thosefaced by many conventional automated speech recognition (ASR) systems.For example, voice mail messages for a mobile telephone frequentlyincludes hesitations, ‘ers’ and ‘ums’. A conventional ASR approach wouldbe to faithfully convert all utterances, even meaningless sounds. Themindset of accurate or verbose transcription characterises the approachof most workers in the ASR field. But it is in fact not appropriate atall for the voice messaging domain. In the voice messaging domain, thechallenge is not accurate, verbose transcription at all, but insteadcapturing meaning in the most helpful manner for the intendedrecipient(s).

Only by successfully addressing all five of these requirements is itpossible to have a successful implementation.

2. Description of the Prior Art

Conversion from speech-to-text (STT) uses automatic speech recognition(ASR) and has, up until now, been applied mainly to dictation andcommand tasks. The use of ASR technology to convert voicemail to text isa novel application with several characteristics that are task specific.Reference may be made to WO 2004/095821 (the contents of which areincorporated by reference) which discloses a voice mail system fromSpinvox Limited that allows voicemail for a mobile telephone to beconverted to SMS text and sent to the mobile telephone. Managingvoicemail in text form is an attractive option. It is usually faster toread than to listen to messages and, once in text form, voicemailmessages can be stored and searched as easily as email or SMS text. Inone implementation, subscribers to the SpinVox service divert theirvoicemail to a dedicated SpinVox phone number. Callers leave voicemailmessages as usual for the subscriber. SpinVox then converts the messagesfrom voice to text, aiming to capture the full meaning as well asstylistic and idiomatic elements of the message but without necessarilyconverting it word-for-word. Conversion is done with a significant levelof human input. The text is then sent to the subscriber either as SMStext or email. As a result, subscribers can manage voicemail as easilyand quickly as text and email messages and can use client applicationsto integrate their voicemail—now in searchable and archivable textform—with their other messages.

The problem with transcription systems that are significantly humanbased however is that they can be costly and difficult to scale to themass-market—e.g. to a user base of 500,000+ or more. Consequently, it isimpractical for major mobile or cell phone operators to offer them totheir subscriber base because for the required fast response times it isjust too expensive to have human operators listening to and transcribingthe entirety of every message; the cost per message transcribed would beprohibitively high. The fundamental technical problem therefore is todesign an IT-based system that enables the human transcriptionist tooperate very efficiently.

WO 2004/095821 envisaged some degree of ASR front-end processingcombined with human operators: in essence it was a hybrid system; thepresent invention develops this and defines specific tasks that the ITsystem can do that greatly increase the efficiency of the entire system.

Hybrid systems are known in other contexts, but the conventionalapproach to voice conversion is to eliminate the human element entirely;this is the mindset of those skilled in the ASR arts, especially the STTarts. We will therefore consider now some of the technical background toSTT.

The core technology of speech-to-text (STT) is classification.Classification aims to determine to which ‘class’ some given databelongs. Maximum likelihood estimation (MLE), like many statisticaltools, makes use of an underlying model of the data-generatingprocess—be it the toss of a coin or human speech production system. Theparameters of the underlying model are estimated so as to maximize theprobability that the model generated the data. Classification decisionsare then made by comparing features obtained from the test data withmodel parameters obtained from training data for each class. The testdata is then classified as belonging to the class with the best match.The likelihood function describes how the probability of observing thedata varies with the parameters of the model. The maximum likelihood canbe found from the turning points in the likelihood function if thefunction and its derivatives are available or can be estimated. Methodsfor maximum likelihood estimation include simple gradient descent aswell as faster Gauss-Newton methods. However, if the likelihood functionand its derivatives are not available, algorithms based on theprinciples of Expectation-Maximization (EM) can be employed which,starting from an initial estimate, converge to a local maximum of thelikelihood function of the observed data.

In the case of STT, supervised classification is used in which theclasses are defined by training data most commonly as triphone units,meaning a particular phoneme spoken in the context of the preceding andfollowing phoneme. (Unsupervised classification, in which the classesare deduced by the classifier, can be thought of as clustering of thedata.) Classification in STT is required not only to determine whichtriphone class each sound in the speech signal belongs to but, veryimportantly, what sequence of triphones is most likely. This is usuallyachieved by modelling speech with a hidden Markov model (HMM whichrepresents the way in which the features of speech vary with time. Theparameters of the HMM can be found using the Baum-Welch algorithm whichis a form of EM.

The classification task addressed by the SpinVox system can be stated ina simplified form as: “Of all the possible strings of text that could beused to represent the message, which string is the most likely given therecorded voicemail speech signal and the properties of language used invoicemail?” It is immediately clear that this is a classificationproblem of enormous dimension and complexity.

Automatic speech recognition (ASR) engines have been under developmentfor more than twenty years in research laboratories around the world. Inthe recent past, the driving applications for continuous speech, widevocabulary ASR have included dictation systems and call centreautomation of which “Naturally Speaking” (Nuance) and “How May I HelpYou” (AT&T) are important examples. It has become clear that successfuldeployment of voice-based systems depends as heavily on system design asit does on ASR performance and, possibly because of this factor,ASR-based systems have not yet been taken up by the majority of IT andtelecommunications users.

ASR engines have three main elements. 1. Feature extraction is performedon the input speech signal about every 20 ms to extract a representationof the speech that is compact and as free as possible of artefactsincluding phase distortion and handset variations. Mel-frequencycepstral coefficients are often chosen and it is known that lineartransformations can be performed on the coefficients prior torecognition in order to improve their capability for discriminationbetween the various sounds of speech. 2. ASR engines employ a set ofmodels, often based on triphone units, representing all the variousspeech sounds and their preceding and following transitions. Theparameters of these models are learnt by the system prior to deploymentusing appropriate training examples of speech. The training procedureestimates the probability of occurrence of each sound, the probabilityof all possible transitions and a set of grammar rules that constrainthe word sequence and sentence structure of the ASR output. 3. ASRengines use a pattern classifier to determine the most probable textgiven the input speech signal. Hidden Markov model classifiers are oftenpreferred since they can classify a sequence of sounds independently ofthe rate of speaking and have a structure well suited to speechmodelling.

An ASR engine outputs the most likely text in the sense that the matchbetween the features of the input speech and the corresponding models isoptimized. In addition, however, ASR must also take into account thelikelihood of occurrence of the recognizer output text in the targetlanguage. As a simple example, “see you at the cinema at eight” is amuch more likely text than “see you at the cinema add eight”, althoughanalysis of the speech waveform would more likely detect ‘add’ than ‘at’in common English usage. The study of the statistics of occurrence ofelements of language is referred to as language modelling. It is commonin ASR to use both acoustic modelling, referring to analysis of thespeech waveform, as well as language modelling to improve significantlythe recognition performance.

The simplest language model is a unigram model which contains thefrequency of occurrence of each word in the vocabulary. Such a modelwould be built by analysing extensive texts to estimate the likelihoodof occurrence of each word. More sophisticated modelling employs n-grammodels that contain the frequency of occurrence of strings of n elementsin length. It is common to use n=2 (bigram) or n=3 (trigram). Suchlanguage models are substantially more computationally expensive but areable to capture language usage much more specifically than unigrammodels. For example, bigram word models are able to indicate a highlikelihood that ‘degrees’ will be followed by ‘centigrade’ or‘fahrenheit’ and a low likelihood that it is followed by ‘centipede’ or‘foreigner’. Research on language modelling is underway worldwide.Issues include improvement of the intrinsic quality of the models,introduction of syntactic structural constraints into the models and thedevelopment of computationally efficient ways to adapt language modelsto different languages and accents.

The best wide vocabulary speaker independent continuous speech ASRsystems claim recognition rates above 95%, meaning less than one worderror in twenty. However, this error rate is much too high to win theuser confidence necessary for large scale take up of the technology.Furthermore, ASR performance falls drastically when the speech containsnoise or if the characteristics of the speech do not match well with thecharacteristics of the data used to train the recognizer models.Specialized or colloquial vocabulary is also not well recognized withoutadditional training.

To build and deploy successful ASR-based voice systems clearly requiresspecific optimization of the technology to the application and addedreliability and robustness obtained at the system level.

To date, no-one has fully explored the practical design requirements fora mass-scale, user-independent, hybrid voice messaging system that canconvert unstructured voice messages into text. Key applications are forconverting voicemail sent to a mobile telephone to text and email; otherapplications where a user wishes to speak a message instead of typing itout on a keyboard (of any format) are also possible, such as instantmessaging, where a user speaks a response that it captured as part of anIM thread; speak-a-text, where a user speaks a message that he intendsto be sent as a text message, whether as an originating communication,or a response to a voice message or a text or some other communication;speak-a-blog, where a user speaks the words he wishes to appear on ablog and those words are then converted to text and added to the blog.In fact, wherever there is a requirement, or potential benefit to begained from, enabling a user to speak a message instead of having todirectly input that message as text, and having that message convertedto text and appear on screen, then mass-scale, user-independent, hybridvoice messaging systems of the kind described in the presentspecification may be used.

SUMMARY OF THE INVENTION

The invention is a mass-scale, user-independent, device-independent,voice messaging system that converts unstructured voice messages intotext for display on a screen; the system comprising (i) computerimplemented sub-systems and also (ii) a network connection to humanoperators providing transcription and quality control; the system beingadapted to optimise the effectiveness of the human operators by furthercomprising:

3 core sub-systems, namely (i) a pre-processing front end thatdetermines an appropriate conversion strategy; (ii) one or moreconversion resources; and (iii) a quality control sub-system.

Further aspects are given in Appendix III. The invention is acontribution to the field of designing a mass-scale, user-independent,device-independent, voice messaging system that converts unstructuredvoice messages into text for display on a screen. As explained earlier,this field presents many different challenges to the system designercompared to other areas in which ASR has in the past been deployed.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will be described with reference to the accompanyingFigures, in which

FIGS. 1 and 2 are schematic views of a mass-scale, user-independent,device-independent, voice messaging system that converts unstructuredvoice messages into text for display on a screen, as defined by thisinvention.

FIGS. 3 and 4 are examples of how the system presents possible word andphrase choices to a human operator to accept or refine.

DETAILED DESCRIPTION

The present invention includes:

A mass-scale, user-independent, device-independent, voice messagingsystem that converts an unstructured voice message into text for displayon a screen; the system comprising (i) computer implemented sub-systemsand also (ii) a network connection to human operators providingtranscription and quality control; the system being adapted to optimisethe effectiveness of the human operators by further comprising: acomputer implemented queue manager sub-system that uses historic data tointelligently manage loading and calling in of resources as required toensure that converted message delivery times meet a pre-definedstandard.

In addition, the present invention comprises the system described abovein which the historic data has been analysed to determine optimaldecision making. Further, the present invention includes the systemdescribed above in which the historic data has been analysed todetermine optimal trade-offs.

The SpinVox system designers faced many challenges:

Automatic Speech Recognition and Language Models

First and foremost, it was clear to the designers that established ASRtechnology on its own was not sufficient to provide reliable STT forvoicemail (and other mass-scale, user-independent voice messagingapplications). ASR relies on assumptions built from theoretical modelsof speech and language including, for example, language modelscontaining word prior probabilities and grammar rules. Many, if not all,of these assumptions and rules are invalid in general for voicemailspeech. Factors found in the voicemail STT application that are beyondthe capabilities of standard ASR technology include:

-   -   voice quality is subject to environmental noise, handset and        codec variation, network artefacts including noise and        drop-outs;    -   users do not know they are talking to an ASR system and are        comfortable leaving a message using natural and sometimes        ill-structured language;    -   the language itself and accent used in the voicemail are not        constrained or predictable;    -   vocabulary variations occur rapidly even within the same        language so that, for example, the language statistics may vary        because of major current affairs events.

IT Infrastructure

The design of the IT infrastructure to maintain availability and qualityof the SpinVox service makes exacting demands on computational power,network and storage bandwidth and server availability. Loading on theSpinVox system is subject to unpredictable peaks as well as morepredictable cyclic variations.

Unconvertible Messages

It is to be expected that a fraction of messages are unconvertible.These might be empty messages, such as ‘slam-downs’, messages in anunsupported language or unintentionally dialled calls.

Quality Assessment

The assessment of quality at each stage of the SpinVox system is initself a challenge. Signal processing provides numerous analysistechniques that can be applied to the speech signal, ranging forstraightforward SNR measurement to more sophisticated techniquesincluding the explicit detection of common artefacts. However, directmeasurements such as these are not significant in themselves but need tobe assessed in terms of their impact on the later conversion process.Likewise, ASR confidence can be measured in terms of the outputprobability of alternative recognition hypotheses but, as before, it isimportant to measure the quality in terms of impact on the overall textconversion and the complexity of the quality control needed to reach it.

User Experience and Human Factors

The value of the system to customers is influenced heavily by the levelof success with which human factors are accommodated by the design.Users will quickly lose confidence in the system if they receive garbledmessages or find the system other than transparent and very simple touse.

The above challenges have been met in the SpinVox system design asfollows:

System Design A simplified block diagram of the SpinVox system design inFIG. 1 shows the major functional units. At the core is the ASR engine1. SpinVox draws a clear distinction between ASR and a full STTconversion. ASR 1 is a subsystem which generates ‘raw’ text, given theinput speech signal. This is a key element used for STT conversion, butis only one of several important subsystems that are needed to achievereliable STT conversion. The front-end pre-processing subsystem 2 canperform broad classification of the speech signal which can be used todetermine the conversion strategy in terms of the choice of acombination of ASR engine, model set and speech enhancement processing.The quality assessment subsystem 3 measures the quality of the inputspeech and the ASR confidence from which a quality control strategy canbe determined. The quality control subsystem 4 operates on the ASRoutput. Its purpose is to generate semantically correct, meaningful andidiomatic text to represent the message within the constraints of thetext format. Knowledge of context, including the caller ID, therecipient and caller-specific language models built up over time can beused to improve the quality of conversion substantially compared to theraw ASR output. The converted text is finally output from postconversion language processing sub-system 5 to an SMS text and emailgateway.

Key Features The main features of the approach adopted by SpinVox are:

-   -   Meaningful Message Conversion

The text conversion captures the message—its meaning, style andidioms—but is not necessarily a word-for-word conversion of thevoicemail.

-   -   Turn-around Time

The turn-around time for conversion of a message is guaranteed.

-   -   Reliability

The system can never send a garbled text message. Subscribers arealerted to unconvertible messages which can be listened to in theconventional way.

-   -   Standard Language

Messages are sent in standard language and are not ‘textified’.

-   -   Wide Availability

The system operates entirely in the infrastructure and makes norequirements on the handset or network other than call divert.

-   -   Adaptive Operation

The system can optimize performance using embedded quality controlstrategies driven by knowledge built up over time from languagemodelling of voicemail in general as well as caller-specific languagemodelling. In addition, the system can choose from a number of possiblespeech-to-text conversion strategies based on the characteristics of thevoicemail message. Voicemail message data and corresponding textconversions are continuously analysed so as to update and adapt theSpinVox STT system.

-   -   Quality Monitoring

The quality of speech-to-text conversion can be monitored at each stageand so quality control, whether by human or automatic agents, can beundertaken efficiently.

-   -   Language Processing

Post-conversion language processing 5 can be performed to enhance thequality of the converted message text, remove obvious redundancies andvalidate elements of the message such as commonly used salutationstructures.

State-Of-The-Art ASR

Commercial ASR engines can be used so as to take competitive advantageof state-of-the-art ASR technology. Different ASR engines can be calledon to handle different messages or even different parts of the samemessage (with the decision unit 2 decided on which engine to use). Thehuman operators themselves could also be considered as an instance of anASR engine, suitable for some tasks but not others.

Stable and Secure

The service is run on highly stable and secure Unix servers and canadapt to the demand for various languages as different time-zonesexperience peaks spread throughout each 24 hour period.

Quality Control SpinVox have developed a detailed understanding ofusers' expectations and desires for telephony-based messaging systems.They have identified zero-tolerance of users to nonsensicalspeech-to-text conversion, particularly where there is evidence that theerrors have been introduced by a machine rather than by human error.Quality control of the converted text is therefore of key importance.Three alternative quality strategies can be used; decision unit 2selects the optimal one. (i) Messages for which the ASR conversionconfidence is sufficiently high can be checked automatically by qualityassessment sub-system 3 for conformance to quality standards. (ii)Messages for which the ASR conversion confidence is not sufficientlyhigh can be routed to a human agent 4 for checking and, if necessary,correction. (iii) Messages for which the ASR conversion confidence isvery low are flagged as unconvertible and the user is informed of thereceipt of an unconvertible message. Unconvertible messages can belistened to by the user, if they wish, using a single key-press. Theoutcome of these strategies is that the SpinVox system is designed sothat a failure to convert is favoured over generating a conversioncontaining errors. User confidence in the system is therefore protected.SpinVox's statistics indicate that a substantial percentage ofvoicemails are successfully converted.

One of the important tools used by SpinVox for improving the quality ofconverted messages is knowledge of the language (common phrases, commongreetings and sign-offs etc) used in voicemail messages. Fromaccumulated data gathered over time, statistical language modelsspecific to voicemail speech can be developed and then used to guide theSTT conversion process. This greatly improves accuracy of conversion fornon-standard language constructions.

The most apparent feature of SpinVox is that it supplies a service thatmany users did not realize they needed but soon find they cannot managewithout. It is the first real-time system that provides speech-to-textconversation of voicemails. Its impact for network operators isincreased network traffic, from improved call continuity, both for voiceand data. The operational success of the SpinVox system has beenachieved by taking a design approach that is driven byquality-of-service first, and technology second. The system design isbased on a detailed understanding of customer expectations of theservice and, even more importantly from an engineering perspective, thestrengths and weaknesses of ASR technology. By exploiting the strengthsof ASR and factoring out the weaknesses by stringent quality control,SpinVox is an effective deployment that meets the practical designrequirements for a mass-scale, user-independent, hybrid unstructuredvoice messaging system.

SpinVox have demonstrated success in delivering a voice-processing-basedservice by focusing its conversion technology on a very specific targetapplication—voicemail conversion. The indication is that system designtargeted to a very well defined application is a more productiveapproach than the seemingly endless search for ever decreasingimprovements in raw performance measures of, for example, ASR engines.This approach opens up the possibility of new application areas intowhich SpinVox's technology components and system design know-how couldbe deployed.

SpinVox has developed significant know-how as system architects forvoice-based applications with in-house expertise covering speechrecognition, telecoms applications, cellular networks and human factors.The opportunities for growth and development in advanced messagingtechnologies are likely to point to enabling the integration of voiceand text messaging, thereby facilitating search, management andarchiving of voicemail with all the same advantages currently enjoyed byemail and SMS text messaging, including operational simplicity andself-documentation. Such developments are paralleled by the convergenceof voice and data in other telecommunications systems.

The Spinvox system is moving from what, from the outside, is a speakerindependent problem towards a speaker dependent problem which is a hugeinsight into making speech work in telephony. Why? Because it is usingthe fact that calls, messaging and other communication is driven bycommunity use—i.e. 80% of voicemails come from just 7-8 people. SMS just5-6. IM just 2-3. Spinvox uses ‘call-pair’ history to do several things:

1. build a profile of what a certain caller says every time they call—aspeaker dependent speaker model—how the caller speaks (intonation, etc.. . . );

2. build a language model of what that caller says to someone—a speakerdependent language model—what the caller says (words, grammar, phrases,etc. . . . );

3. in 1 & 2 we are really building a language model of how A speaks toB. This is more refined than just how A speaks in general. It isatypical to the messaging type (i.e. how you speak in Voicemail) and itis also atypical to how you speak to B (e.g. the way a person speak amessage to his mother is very different inintonation/grammar/phrases/accent/etc. . . . than when he speaks to hiswife).

4. Spinvox is building speaker-receiver pair models that go from generalspeaker/language independence to dependent without any user input ortraining;

5. Spinvox has the ability to use both party's language to each other(e.g. how I call back and leave a message) to further refine relevantwords (e.g. dictionary), grammar/phrases, etc. . . .

Further details on these aspects of the Spinvox Voice Message ConversionSystem are given in Appendix I below.

Appendix I

SpinVox—Voice Message Conversion System

The SpinVox Voice Message Conversion System (VMCS) focuses on onething—conversion of spoken messages into meaningful text equivalent. Inthis, it is unique as are the methods and technologies advanced herein.

Concept

A novel method of converting voice-messages into text using multi-stageAutomatic recognition techniques and human assisted Quality Control andQuality Assurance techniques and processes. The automated and humanelements interact directly with each other to generate live/real-timefeedback which is core to the system always being able to learn fromlive data to stay in-tune and deliver consistent quality. It is alsodesigned to take advantage of the inherent limits of AI (ASR) andgreatly improve accuracy by use of contextual bounds, human guidance,and language data live from the Internet.

Issue

Traditional approaches to speech conversion have been very much gearedat the Recogniser level and creating high quality automatic speechrecognition in laboratory conditions where inputs are highly controlledand guarantee a high level of accuracy.

The problem is that in the real world, speech recognition has many otherelements to contend with:

-   -   Random speakers—anyone can use it    -   Noisy input—background noise and poor quality of speaker    -   Poor and variable transmission quality with lossy compression        and bad mobile handset connections    -   Grammatically incorrect speech, slang, or highly localised        expressions    -   Contextual sensitive grammar or implied meaning from a unique        context between message creator and recipient    -   Context changes within a message—context boundaries—that        invalidate the use of normal grammar rules

to cite a few and they all constantly vary with time, so the actualsource input isn't a defined problem in time, but a constantly evolvingone.

Solution

Key is to correctly define the problem: conversion of spoken messagesinto meaningful text equivalent.

This does not mean perfect, verbose transcription, rather the mostimportant pieces of the message presented in easily understandable form.Accuracy measures are both quantitative and qualitative as the ultimatescore is User Rated Accuracy where the SpinVox VMCS scores a consistent97%.

There are two key parts:

-   -   Use a constant, live feed-back mechanism for the learning        system, human driven    -   Use contextual information to better define each conversion        problem

Using contextual information helps the system better estimate thelikelihood of something being said in a message given the message's:

-   -   Type    -   Length    -   Time of day    -   Geography    -   Caller context—both caller and recipient (call-pair history)    -   Recent events    -   Etc. . . .

and known language structure most likely to occur in certain messagetypes—Natural Language, described below.

Natural Language

When analysing voice messages and spoken text messages, regular patternsoccur in what people say, how they say it and in what order—NaturalLanguage. This clearly varies by message type or context, so forordering Pizza, it would be different.

For example, in voicemail, how people greet can be well defined with 35or so of the most common expressions—“Hi, it's me”, “Hello, it's Danielhere”, “Hiya. How you doing?”, “Watcha mate”, “Alright?”, etc. . . . andlikewise good-bye's can be well defined by common expressions—“Ok, bye”,“Cheers”, “Cheers mate”, “Thanks now. Bye. Cheers”, etc. . . .

Clearly, different parts of a spoken message have implicit meaning andtherefore using this key we can improve recognition accuracy by usingthis context to select the most likely classification of what wasactually said.

Building these into large statistically related models is what isdefined as our Natural Language Model, one for each language, includingdialects within any language.

Context Vectors

Natural language is often governed by its context, so when convertingthe body of a message, the context of what has been said can be used tobetter estimate what was actually said—e.g. a call to a booking linewill more likely see expressions related to enquiring and some specificnames of the company, product and price, versus a call to a home phonenumber where friendly expressions regarding greetings, ‘how are you’,‘call me back’, etc. . . . are much more likely.

Within voice-messages, we can use context vectors to better estimate thelikely content and natural language set that applies:

-   -   CLI (or any party identifier) is a very powerful context vector        -   Can tell from the number's geography likely language/dialect            and regional real nouns most likely to be used        -   Can tell if number is a known commercial number and            therefore message type better predicted—e.g. calls from 0870            no.s are commercial and therefore high chance this is a            business message, whereas from 07 range is from a personal            mobile, so time of day will drive message type more between            business, personal, social or other.        -   Allows you to better get their number right if spoken inside            the message        -   Is a key from which you can build history and known            dictionary/grammar—e.g. always says ‘dat's wicked man’ in a            street accent        -   Can build a speaker dependent recognition system—i.e. we can            tune the ASR to you as a particular caller and get much            higher recognition accuracy, your own vocabulary, grammar,            phraseology, lexicon and general natural language    -   Call Pair History—deeper use of CLI (or any party identifier)        -   You can train the system far more accurately to a call            pair's message history        -   You can train to A party's (caller) voice regardless of B            party (recipient)        -   You can train to subject area and language A party uses with            B party        -   You can train multiple A & B party relationships and drive            the system to higher accuracy and speed    -   Time of day, day of week        -   Voicemail traffic rates, average message length and content            type vary with time of day in each language market, from            very business-like messages during peak hours (8 am-6 pm) to            more personal (7-10 pm), to highly social (11 pm-1 am), to            very functional (2 am-6 am). This also varies by day of the            week so a Wed is the busiest day and contains highest level            of business messages, but Sat & Sun have a very different            profile of message type (largely personal, chatty messages)            that need to be treated differently.    -   International numbers        -   By parsing the country code (e.g. 44, 33, 39, 52, 01) we can            better determine language and dialect.    -   Available customer data        -   Customer name, address and possibly work place.

Implied Context Between A & B Party

Taking this on step further, there are many other very important cluesthat can help us better estimate the likely content of a message,particularly those that relate to who the two parties are, what thelikely purpose of the message is, and where they're calling from or to.

In voicemail-to-text and spoken text, we know that having the caller'snumber

-   -   Allows us to better estimate any number left inside the message    -   Build history of known words, expressions, phrases, etc. . . .        between the two parties    -   Likely language (e.g. call from +33 to +33 will most likely be        in French, but +33 to +44 may have a 50% chance of being in        French)    -   Names and their correct spellings

If you know the history of the A party's calls/messages, and theirhistory of messages for the B party, you can build a speaker dependentprofile and gain large improvements from your recogniser and itsgrammar.

Conversion Quality

In addressing this problem, defining the actual required outcome isessential as it makes a big difference in your approach to solving howto convert voice-messages (voicemails, spoken SMS, Instant messaging,etc. . . . ) to text and how to optimally apply the conversion resourceyou have.

When someone leaves us a voice message, the purpose is a message, not aformal written piece of communication, so less accurate conversion willbe tolerated as long as the meaning of the message is correctlyconveyed.

In addition, there is asymmetry so that the message depositor isn'tcomparing what they said with the converted text. With the context ofwho called, the recipient is reading the converted output with theobjective of finding out what the message is, so the requirement isexcellent message extraction for conversion, not a verbose(word-for-word, utterance-for-utterance) conversion. In fact,conversely, a verbose conversion, unless well dictated, is oftenperceived as a low quality message as it contains lots of inelegant andunwanted pieces of spoken message language (e.g. uhmms, ahhs, repeats,spellings of words, etc. . . . )

Therefore, quality in this context is about extracting the importantelements of a message—Intelligent Conversion.

At it's simplest, there are three key elements that provide the mostmeaning and hence are essential to achieving message quality:

-   -   1 Who is it from—huge value in understanding meaning from this        context    -   2. What's the purpose of the message—e.g. call me back urgently,        running late, change of plan/timings, call me on this no., just        to say hi, etc. . . .    -   3. Any specific facts, the most common being:        -   a. Names        -   b. Numbers, phone numbers        -   c. Time        -   d. Address

Other information in the message is largely there to support conveyingthese key elements and often helps provide better context for these keyelements.

Varying Quality Sensitivity Within Message

What's also very important to understand is that we need to recognisethat each key part of any message has a different role in delivering themessage and we can therefore attribute another dimension of quality toeach that we should aim to achieve during conversion.

Messages can be broken down into

-   -   Greeting (top)    -   Message (body)    -   Good-bye (tail)

The percentage of messages that contain any body is clearly a functionof deposited message length, so we know that short messages (e.g. sub 7seconds) typically only contain a Greeting and a Goodbye. Above this,the probability of a meaningful message body grows exponentially. Thisfact also helps us better estimate the likely conversion strategy weshould use.

Greeting & Good-bBes

How someone greets you can be classified into a some 50 commonlyrecognised salutations (e.g. Hi there, hey it's me, Hi, this is Xcalling from Y, Hello, I'm just calling to . . . , etc. . . . ).Likewise, the ‘good-bye’ element of a message can be classified into asimilar order of commonly recognises good-byes (e.g. Thanks very much,see ya, Ta, good bye, Cheers now, see you later, etc. . . . )

Two issues dictate our conversion quality requirement:

-   -   1 Greetings and Good-byes are there for message protocol and        often contain little of value for the main message, so our        tolerance to low accuracy is high, provided that it makes sense.    -   2. We can classify the vast majority of Greetings and Good-byes        into some 50 commonly recognised categories each.

Therefore, the quality requirement during a Greeting, a salutation orGood-bye is far less than what's contained in the message body, normallythe point of a message or a key fact—e.g. call me on 020 7965 2000

Message Body

The message body naturally has a higher quality requirement, but itlikewise can often be found to contain regular patterns of naturallanguage that relate to the context and we can therefore also apply adegree of classification to better help us attain the correct answer.

A good example is:

“Hi Dan, John here”—Top of message (or greeting)

“can you give me a call back on 0207965200 when you get this”—Body ofmessage

“Thanks a lot mate. Cheers. Bye bye.”—Tail of message (or good-bye)

In this case, the Body is a well structured piece of Voicemail Languagethat the SpinVox Conversion System has learnt. It can then break themessage down and correctly assign the body outcome.

Elements in this case that apply are:

-   -   A & B party known    -   Phone number is John's CLI, or seen before in his calls to        others    -   Message length—sub 10 seconds, so more likely to be common        expression    -   Time of day—working hours—John normally doesn't leave detailed        messages in working hours, just short, simple messages.

SpinVox Voice Message Conversion System

Having correctly stated our problem and identified some very importantfeatures of speech and how it relates to the text equivalent, theSpinVox system (see FIG. 2) was designed to take full advantage ofthese:

Spin Vox Voice Message Conversion System

This diagram shows the three key stages that enable us to optimise ourability to correctly convert voice-messages (voicemail, spoken SMS,Instant messages, voice clips, etc. . . . ) to text.

A key concept is that the system uses the term Agent for any conversionresource, whether machine/computer based or human.

Pre-Processing

This does two things:

-   -   1. Optimises the quality of the audio for our conversion system        by removing noise, cleaning up known defects, normalising        volume/signal energy, removing silent/empty sections, etc. . . .    -   2. Classifies the message type for optimally routing the message        for conversion, or not.

Classifying the message type is done using a range of ‘Detectors’:

-   -   Language        -   e.g. English UK/US/Auz/NZ/S.Africa/Canadian and then to            dialect types within it it (e.g. within UK—S.East, Cockney,            Birmingham, Glasgow, N.Ireland, etc. . . . )        -   Allows us to determine if we support the language        -   Allows to select which conversion route to use: QC/QA            profile, TAT rules (SLA), which ASR stage strategy            (engine(s)) to load and which post-processing strategy to            apply

Methods:

-   -   Statistical Language Identification        -   Prior art:            -   several methods of automatic language identification                known        -   SpinVox solution:            -   base decision on context: knowledge about registration,                location and call history of caller and receiver    -   Signal Based Language Identification        -   Problem with Prior art:            -   high accuracy methods require large-vocab speech                recognition or at least phone recognition, hence                expensive to produce and run            -   requirement for reliable and fast method based purely on                recordings (labeled with language but nothing else)        -   SpinVox solution:            -   1. automatically cluster speech data for each language                (vector quantization)            -   2. combine cluster centres            -   3. use statistical model of sequence of clusters for                each language to find best match            -   4. build model of relationship between score differences                between models and expected accuracy            -   5. combine several versions of 1-4 (based on varying                training data, feature extraction methods etc) until                desired accuracy is achieved    -   Noise—SNR detector        -   If the amount of noise in a message is above a certain            threshold, then it becomes increasingly difficult to            correctly detect the message signal and convert. More            meaningful is if the ratio of the signal-to-noise drops            below a certain level, then you've a high degree of            confidence you won't be able to convert the message.        -   SpinVox users value the fact that when they receive a notice            that the message was unconvertible, the source audio is so            poor that over 87% of the time they call or text the person            directly back and continue the ‘conversation’.    -   Speech Quality Estimator        -   If the quality of someone's speech is likely too low for            either the conversion system or agent to use. OR, content            that a user ought to listen to themselves—e.g. someone            singing them a happy birthday        -   SpinVox solution includes:            -   1. find drop-outs (voice packets lost during                transmission) based mainly on zero-crossing counts            -   2. also estimate noise levels            -   3. calculate overall measure of voice quality and use                adaptive threshold to reject lowest quality messages    -   Hang-up (‘slam-down’) detector        -   Messages where someone called, but left no meaningful audio            content. Typically short messages with background            utterances.    -   Inadvertent Call detector        -   Typically a call from the redial button being pressed whilst            in someone's pocket and leaving a long rumbling message with            no meaningful audio content in it    -   Standard messages        -   Pre-recorded messages, most common in US, from an            auto-dialling system, or service notices or calls.    -   Greet & Good-bye        -   If the message only contains these, then we can use a            dedicated piece of ASR to correctly convert these messages    -   Message length & speech density        -   Length allows us to initially estimate the likelihood of            message type—e.g. short call normally just a simple ‘hi,            it's X, call me back please’ vs. long call which will            contain something more complex to convert        -   Speech density will allow you to adjust your estimate of how            likely a message length is to be a good indicator of            type—e.g. low density and short message is likely to be just            a simple ‘hi, it's X, call me back please’, but high            density, short message will skew this towards you needing a            higher level of conversion resource as message complexity            will be higher.

Clearly, pre-processing allows us in certain cases (e.g. slam-down,inadvertent call, foreign/unsupported language) to immediately route themessage as classified and send the correct notification text to therecipient (e.g. ‘this person called, but left no message’), saving anyfurther use of precious conversion system resources.

Automatic Speech Recognition (ASR)

This is a dynamic process. The optimal use of conversion resources isdetermined at a message level.

We take input from both the Pre-Processing stage on messageclassification and from any context vectors and use these to choose theoptimal conversion strategy. This means that this stage is using thebest ASR technology for the particular task. The reason is thatdifferent types of ASR are highly suited to specific tasks (e.g. one isexcellent for greetings, another for phone numbers, another for Frenchaddresses).

This stage is designed to use a range of conversion agents, whether ASRor humans, and only discerns between them based on how the conversionlogic is configured at that time. As the system learns, this is adaptedand different strategies, conversion resources and sequences can beemployed.

This strategy isn't only applied at the whole message level, but can beapplied within a message.

Top'n'Tail

A strategy is to sub-divide the message's Greeting (top), Body andGoodbye (tail) sections, send them to different pieces of ASR that areoptimal for that element of a message. Once they've completed, they'rere-assembled as a single message.

Number Routing

Another strategy is to sub-divide out any clear elements where phonenumbers, currencies or obvious use of numbers is spoken in the message.These sections are sent to a specific piece of ASR or Agent for optimalconversion, then reassembled with the rest of the converted messages.

Address Routing

Likewise, sub-dividing out elements where an address is being spoken canbe sent to a specific piece of ASR or Agent, and to an addressco-ordinate validator to ensure that all pieces of an address convertedare real. E.g. if you can't detect the street name, but have a clearpost-code you can complete the most likely street name. The accuracy offinding the street name is improved by reprocessing the address again,but with your estimated a priori street name refining your ASRclassification variables to a much more limited set and seeing ifthere's a high match or not.

Real Noun Routing

Real nouns are renowned for making ASR unreliable. Again, but focusingon just this part and applying much more specialised, butcomputationally more expensive resource, you can much better estimatethe real noun.

Post Processing

The ASR stage contains its own dictionaries and grammar, but this isn'tsufficient to correctly convert the many complex ways we speak. ASR isvery much geared at the word level conversion and very short sequencesof words for estimating likelihood of word sequences and basic grammar(n-gram and trellis techniques). One problem is that mathematicallyspeaking, as you widen the set of words you try to estimate the possiblecombinations of beyond 3 or 4, the permutations become so large thatyour ability to pick the right one diminishes faster than any gain madeby widening the number of sequential words, so it's currently anunreliable strategy.

A very good method is to look at wider phrases or sentence structuresthat occur in Natural Language. Approaching the problem from the macrolevel, you can estimate better solutions for errors or words/sectionswhere the ASR confidence is low.

However, this too has its weaknesses. As mentioned, human speechcontains much noise, artefacts and because of the intimate relationshipbetween A and B parties, is prone to large contextual boundaries. Somethings make no sense to anyone other than to people who have a huge setof context in which to make much sense out of seemingly random phrasesor unreliable sounding words.

For instance, “see you by the tube at Piccadilly opposite the Trocaderoand mine's a skinny mocha when you get here” would make no sense toanyone unless they knew the possible meaning of ‘tube’, they'd been toLondon and knew that Piccadilly has a building called the ‘Trocadero’very close by and understood that in Starbucks nearby they serve a drinkcalled a ‘mocha’ and it's low fat, i.e. ‘skinny’.

Real-World Corpi—Context Check

One solution is to look at a very large corpus of English words,real-nouns, phrases, sayings, regular expressions to check that yourconversion might have contained these sequence of words.

The problem is that in normal speech, there are enormous possiblecombinations of these, and critically, this lacks any real-world contextcheck. How do you know that the combinations of real nouns Piccadilly,Trocadero, mocha and skinny are valid, let alone good conversions ofyour source audio? The only absolute is a real-world check andunfortunately, by definition, we humans are the only ones at the momentable to qualify whether something has real-world validity or not—we doafter all still program computers and databases they rely on.

With human level intelligence, you can most accurately check whetherthese seemingly unconnected items have any likely context in the realworld. However, humans also lack in complete knowledge of everythingwhich is why a significant percentage of Londoners wouldn't becomfortable knowing whether this phrase was likely or not given theirknowledge of Piccadilly.

A solution is to use the planet's largest corpi of human knowledge. Thebillions of pages and databases created by human editors available onthe Internet. A simple query as to whether the sentence, or any elementof your conversion, is cited on the internet gives you a highlyqualified real-world test of whether this is something humans havelikely experienced and recorded and therefore might be real. So in theabove example, we find that Google, Yahoo!, MSN and other major searchengines are able to give enough page hits with these in that we have ahighly improved confidence that our conversion is indeed correct.

Further, using the Internet, we can most often find the correct spellingof phonetic approximations of words, real-nouns and place names that ASRattempts with new or unknown words if comes across. Currently, this isdone through enormously time consuming and expensive manual programmingof the ASR's dictionaries.

The other extremely valuable benefit of this solution is that theInternet is a live system that very accurately reflects currentlanguage, which is an evolving and dynamic subject and can vary with asingle news headline, so you're not reliant on constantly updating yourASR dictionaries with a limited sub-set of natural language, but haveaccess to probably the planet's most current and largest source ofnatural language.

EXAMPLE

SpinVox converts the following audio:

Message from English person—

Audio : “The cat sat on Sky when Ronaldo scored against Cacá”

Converted text:

The cat sat on sky when Rownowdo/Ron Al Doh/Ronaldow/Ronahldo scoredagainst Caka/Caca/Caker

Problems:

-   -   ‘sat on sky’ is grammatically incorrect—you can't sit on ‘sky’        in dictionary context    -   Rownowdo/Ron Al Doh/Ronaldow/Ronahldo are possible solutions for        an unusual real noun    -   Caka/Caca/Caker are guesses of a highly unusual real noun        Searches on Google for trouble elements of this phrase show:    -   Sky is a brand name—no.1 rank for ‘on Sky’. Therefore, it is a        real noun for an object, so ‘The cat sat on Sky’ is possible        grammatically and valid    -   The first name is most likely Ronaldo just from spell checks        alone of all versions (Google's “Did you mean: Ronaldo?”)    -   Ronaldo is highly correlated with ‘Ronaldo scored’ as he's a        very famous football player and the search returns a large        number of exact matches for this phrase    -   The second name is most likely Cacá, because Caca has most hits        for ‘scored against Cacá’.    -   We further our confidence by searching for ‘football        Cacá’—football being derived from the context of ‘Ronaldo        scored’—and we get a large number of highly correlated search        results. Given ‘Ronaldo scored’ has already returned a large        number of successful searches, we are more confident that ‘Cacá’        is the most likely fit.    -   Further, the real-world nature of data indexing of Google means        that terms being used today, current terms, get higher rankings        than less current terms, which is essential in getting speech        recognition to work for current language and context.

Queue Manager

The Queue Manager is responsible for:

-   -   Determining what should happen to a voice message at each        stage—conversion strategy    -   Managing the decision of each Automated stage when it requires        human assistance        -   If at any stage of the automated conversion confidence            intervals or other measures suggest any part of a message            won't be good enough, then the Queue Manager directs this to            the correct human agent for assistance.    -   Guaranteeing our Service Level Agreement with any customer by        ensuring we convert any message within an agreed time—Turn        Around Time (TAT)        -   typically TAT is an average of 3 mins, 95% within 10 mins,            98% within 15 mins.    -   Making decisions by calculating trade-offs between conversion        time and quality. This is a function of what the SLA allows,        particularly to deal with unexpected traffic or abnormal        language use spikes and performance to-date.

This is achieved by use of large state-machines that for any givenlanguage queue can decide how best to process the messages through thesystem. It interacts with all parts and is the operational core of theSpinVox VMCS.

Quality Control Application

Appendix II contains a fuller description of this Lattice method as usedwithin the SpinVox Quality Control Application.

As shown in the Voice Message Conversion System (VMCS) FIG. 2 diagram,human agents interact with messages at various stages. They do thisusing the Quality Control Application.

They also use a variant of this tool to randomly inspect messages toensure the system is correctly converting messages as one of theproblems with AI is that it is unable to be sure that it really isaccurate.

A key inventive step is the use of humans to ‘guide’ the conversion ofeach message. This relies on the SpinVox VMCS databases, which contain alarge corpus of possible matches, ASR and a human inputting a few wordsto create predictive typing solutions. In its extreme case, no human isrequired and the conversion is fully automatic.

Issue

ASR is only good at word level matches. To convert a meaningful message,phrases, sentences and grammar for spoken messaging is necessary. ASRwill produce a statistical confidence measure for each word level match,and phrase where available. It is unable to use context or naturallanguage rules to complete the process of a meaningful and correctconversion.

What automated systems are good at is spelling and basegrammar-consistency.

What humans are good at are meaning, context, natural language, spokengrammar, dealing with ambiguous input and making sense of it. Humanstend to be inconsistent with spelling, grammar and speed.

Business Issue

Using humans costs money, so anything that can be done to use them foronly the things that matter, and hence of economic value, is essential.

SpinVox VMCS uses the concept of Agent Conversion Ratio (ACR)—the ratioof the time it takes and agent to actually process a message to thelength of the spoken message. Anything that reduces ACR and improvesmessage conversion quality is a business driver as a 1% reduction in ACRleads to at least a 1% improvement in gross margin. In fact, thesensitivity is even higher as not only is direct cost of goods soldreduced, but management overhead and operational availability of theservice and scalability all benefit from fewer humans required.

Solution

Lattice method: use human agents to guide the system to pick the correctset of words, phrases, sentences, messages from a pre-determined list ofmost likely options.

The SpinVox VMCS databases hold a rich history of message data as largestatistical models (dictionaries and grammar with context vectors thatrelate them) that can be drawn upon in two key ways:

Lattice Method

-   -   i. The VMCS language model uses context (e.g. call pair history,        language, time of day, etc. . . . —see Context Vectors) to pick        the most likely conversion (the Proposed Conversion) to show to        the agent.    -   ii. As the message plays back, the agent selects either a letter        to choose an alternative (can be just the first few letters of        the correct word), or hits ‘accept’ to accept the proposed        section of text and move on to the next section.    -   iii. As the agent types changes, the system uses this as both        input to pick the new most likely conversion and as feedback        (learning) so that the next time it is more likely to get the        right match first time.    -   iv. What would normally require an agent to type a full message        worth of characters (e.g. 250), only takes a few key-strokes to        complete and in real-time or faster.    -   v. The agent output is now constrained to correct spelling,        grammar and phraseology, or rules about these that control        quality and better message meaning.

This can be presented to the agent in two key ways:

1. ASR assisted Proposed Conversion

In this case, ASR is first used to better predict which text should bethe Proposed Conversion for the agent. It uses what's actually in theaudio of the spoken message to reduce the set of possible conversionoptions to a minimum, thus improving accuracy and agent speed.

-   -   a. ASR can be used for the initial proposed conversion    -   b. ASR can then be continuously used as the agent inputs        selections to further refine the remaining sections of the        proposed conversion

Prior art: Humans correcting transcriptions with choice of wordalternatives

Problem with prior art:

-   -   Corrections still time consuming    -   ASR engine could have made better decision (later in utterance)        if the user correction had been known during decoding

2. Full predictive text typing

Just like 1. above, but where no ASR is used to select the ProposedConversion shown to an agent. This is different to standard predictivetext editors as it relies on specific history (VMCS language models anduse of context vectors—e.g. call pair history) and works at phrase leveland above.

Prior art: predict most frequent word (list of alternatives) givenpartial human input

Problem with prior art:

-   -   Most frequent word very often not the one the user wants    -   Predictions just for one word

In either case, the SpinVox VMCS language models are trained purely byhumans, or by a combination of ASR and humans.

In the extreme case, the system is fully trained and is able to alwayspick the right Proposed Conversion first time and only require humanassistance for Quality Assurance to randomly sample and check the VMCSis correctly self-governing.

Appendix II—Lattice Method

Assorted Observations and Assumptions

-   -   1. Given the large vocabulary and the varying audio quality, it        seems impossible to achieve high enough speech recognition        accuracy for fully automatic conversion for more than a tiny        fraction of utterances. Reliably detecting this fraction, i.e.        deciding that no human check is needed is a very interesting        longer-term research problem but probably not a realistic option        in the short term.    -   2. While a good operator has a target ACR of 3-4, the average is        more like 6-8.    -   3. Correcting an utterance that is already 90% correct takes        about 1.2. (Source: SpinVox Operational Research 2005)    -   4. 75% of correction time is spent on finding and selecting        errors (Wald et al).    -   5. Word selection lists (alternatives) reduce listening time        (Burke 2006).    -   6. Errors tend to cluster (Burke 2006).    -   7. Double speed playback keeps intelligibility and users appear        to prefer it after short training (Arons 97).    -   8. Removing pauses and 50% faster playback give real-time factor        1/3 (Arons 97).    -   9. According to Bain et al 2005, normal typing has ACR 6.3 which        equals ACR for editing ASR output with 70% accuracy. Shadow        transcription is mentioned as “viable” for live subtitling.

Approach

The main aim has to be reducing the agent conversion ratio (ACR) byusing speech technology to support the agent. This can be achieved in anumber of ways:

-   -   1. Let the agent make the decisions we can't afford to get        wrong, i.e. the overall meaning of the message or individual        phrases. The machine can fill in the details.    -   2. Offer predictions while the agent types/edits the utterance.        This might not just save typing time but also helps avoiding        spelling mistakes.    -   3. Provide (simplified) capitalization and punctuation        automatically so that the agent doesn't need to deal with these        issues.

Call Handling Steps

-   -   1. Agent listens to message at high speed (e.g. ½ real time).    -   2. Agent presses button to select category (e.g.        “please_call_back”, “just_calling_back”, . . . “general”).    -   3. In some cases, the utterance is immediately accepted. This        will happen if the message follows a simple pattern defined for        the message category, the speech quality was good and there are        no important but easily confusable parts in the message (e.g.        times).    -   4. System proposes converted string, agent edits while system        continuously (and instantly) updates proposed utterance using        predictions based on speech recognition results.    -   5. Agent presses key to accept utterance as soon as the        displayed utterance is correct.

An example for call handling step 4 is shown in FIG. 3.

In this example, the agent would need 35 key strokes to edit anutterance with 17 words and 78 characters:

-   -   15*<accept_word> (e.g. tab)    -   14*<accept_char> (e.g. right arrow)    -   6*normal input    -   1*<accept utterance> (e.g. Enter)

Most of them should be very quick because the same key has to be presseda few times. Only 6 of them require selecting a normal key.

Note that only 6 of the 17 words (35%) were correct in the utteranceoriginally proposed by the system.

Implementation

Processing Steps

-   -   1. A Speech Recognition engine (e.g. HTK) converts the utterance        speech file into a lattice (i.e. word hypothesis graph—a        directed, a-cyclic graph representing a huge number of possible        word sequences).    -   2. The lattice is re-scored to take into account phone number        (pair) specific information (e.g. names, frequent phrases in        earlier calls etc).    -   3. The lattice is augmented to enable very fast search during        the editing phase (e.g. the most likely path to the end of the        utterance is computed for each node and the arc starting this        path stored, character-sub trees are added to each node        representing decision points). “Families”, i.e. several arcs        differing only in their start and end times are combined within        certain limits.    -   4. When the agent selects a specific category (step 2 in “Call        handling steps”), a corresponding grammar and language model are        selected for parsing and dynamic re-scoring. When the category        is “general”, an unrestricted “grammar” is used.    -   5. The highest scoring path through the lattice matching the        selected category grammar (if appropriate) is selected.    -   6. The result found in this way will be accepted immediately if:        -   a. The category is not “general”.        -   b. The score difference to the highest scoring unconstrained            path is within a given range. This range can be used as a            parameter to dynamically control the tradeoff between speed            and accuracy.        -   c. According to the grammar used to find the path, the            utterance does not contain crucial parts that are easily            confusable (e.g. times).    -   7. When the user accepts words or characters, the system moves        along the selected path through the lattice.    -   8. As characters and words are accepted or typed, their color or        font changes.    -   9. When the agent types something, the system selects the        highest scoring path (again taking into account the current        grammar and possibly other, e.g. statistical information) that        starts with the character(s) typed. This new path is then        displayed.    -   10. When an agent types a word not found in the lattice, it is        automatically spell-checked and correction is offered if        appropriate.    -   11. After the agent presses <accept_utterance>, the text is        processed to add capitalization and punctuation, correct        spelling mistakes, replace number words by digits etc. This uses        a robust probabilistic parser using grammars semi-automatically        derived from the training data.

Audio Playback

The nodes in the lattice contain timing information and hence the systemcan keep track of the part of the message the agent is editing. Theagent can configure how many seconds the system will play ahead. If theagent hesitates, the system plays back the utterance from the wordbefore the current node.

Refinement Options

Mark Important and Unimportant Parts

i. Depending on the relevant category and grammar, specific parts of thedisplayed utterance text that are deemed crucial are highlighted whileparticularly unimportant parts (e.g. greeting phrases) are shaded out.

Use Phrase Classes for Unimportant Parts

Parts of the message are displayed as phrase classes instead ofindividual words. The agent only needs to confirm the class while thechoice of the individual phrase is left to the ASR engine because amistake in this area is considered unimportant. For instance, the class“HEY” could stand for “hi, hay, hey, hallo, hello” and the earlierexample could be displayed as shown in FIG. 4. In this version, the“<accept_word>” key applied to a phrase would accept the whole phrase.Typing a character changes back to word mode, i.e. the phrase classmarker is replaced by individual words.

Limit Prediction Display

Displaying the wrong predictions might actually confuse the agent and itmight be worth displaying only those (partial ones) the system isrelatively certain about.

Alternatively, the relative confidence in various predictions could becolor-coded in some way (cf. “confidence shading”), e.g. uncertain ones(usually further away from the cursor) are printed in very light graywhile more reliable ones are shown darker and bolder.

Utterance Segmentation

Longer silence periods are detected and used to break the message upinto segments. The user interface reflects the segmentation and an extrakey is assigned to “<accept_segment>”. This enables confirming largerphrases with one key press and also resynchronization if the agent typesa word not extending the current path through the lattice.

Keep Cursor on Left Side of Screen

Have a big area in the middle of the screen that shows the currentphrase in large letters. As editing goes on, move the text keep thecursor in same position). Show only a few words left of the cursor. Aswords drop out of the middle area, they move to the top area (smallerfont, gray). More phrases are displayed below, again small and gray.

Show Phrase Alternatives

Always or after key press, show alternative phrase completions like dropdown menu right of cursor and allow selection with arrow keys. Thismeans the agent doesn't need to think about the first characters of thecorrect word which should help for difficut words.

Move Cursor With Speech

As the message is played back, the word spoken is highlightedautomatically and the cursor is moved to the start of the word.

Play Highlighted Region

The agent can select a region (e.g. left and right mouse key) and thesystem keeps playing the segment between the markers until the agentmoves on.

Shadow Transcription for Individual Words or Phrases

Words are highlighted as they are played to the agent and in addition totyping, the agent can simply say the word to replace the currentlyhighlighted one (and the rest of the phrase). The system dynamicallybuilds a grammar from the alternative candidates in the lattice (wordsand phrases) and uses ASR to select the correct one. This is atechnically difficult option because ASR needs to be used from withinthe QC app and the appropriate speaker-dependent models need to betrained and selected at run time.

Accuracy Considerations

Most Likely Result

The accuracy of the highest scoring result after the speech recognitionstep is expected to be rather low (e.g. 25%). So the result displayedinitially will only rarely be correct. IBM reports a word error rate of28% for voice mail in Padmanabhan et al 2002.

When an utterance category can be identified (guessing: 20% of cases),the chance of getting a correct overall result should be reasonably high(say 70%) if the “phrase class” approach is used, i.e. if errors in theexact phrases used for top and tail are accepted and either there are nodifficult parts in the message or they can be verified using otherinformation (phone owners, previous calls). A rough guess would be thatoverall about 10% of utterances could be handled with just one key-press(the one needed for category selection).

Error Correction

It has been observed that speech recognition errors tend to occur inclusters, e.g. the average number of subsequent words containing anerror is about 2 (TODO: find reference). This is usually due to:

-   -   segmentation errors—the first incorrect word is shorter or        longer than the correct one and hence the next word must be        wrong as well    -   the influence of the language model    -   possibly co-articulation modeling

This observation motivates the expectation that a correction of one wordduring the editing process will typically correct more than one mistakein the utterance hypothesis.

Very roughly speaking, typing one character limits the number ofcontenders for the next word by a factor of 1/26. Two characters limitit to 1/676 and should almost certainly exclude all higher scoringincorrect ones. This motivates another prediction: one ASR error shouldin the average require not more than one keystroke to correct.

Best Path Through Lattice

A very important factor for the success of the system is the percentageof lattices containing the correct path even if it has a comparativelylow score. If the correct path is not in the lattice, the system will atsome point not be able to follow a path through the lattice and hence itwill be difficult to produce new predictions. The system might need towait for the agent to type two or three words before finding appropriatepoints in the remaining lattice again to create further predictions.

The size of the lattice and hence the chance of getting the correctutterance can be controlled by parameters (number of tokens and pruning)and in theory the whole search space could be included. This wouldproduce huge lattices, however, that could not be transmitted to theclient within an acceptable time frame. Furthermore, we have to dealwith the occasional occurrence of previously unseen words that wouldconsequently not be in the vocabulary. After a few months of operation(and hence data collection) a rate of about 95% seems achievable.

If the “Utterance segmentation” version described above is used, thesegments would provide easy points for re-starting prediction.

Linguistic Post-Processing

It might be worth defining a simplified “SpinVox message” syntax foreach language. SMSs are generally not expected to contain full, propersentences and rather than attempting to add a lot of punctuation (andoften getting it wrong), it might be worth to use it rarely butconsistently.

Capitalization

This is comparatively easy in English but more difficult in otherlanguages (e.g. German).

Expected Benefits

-   -   1. While converting or editing, the system keeps track of where        in the utterance the agent currently is and hence audio playback        can be controlled better.    -   2. For a certain proportion of utterances, where the agent only        needs to determine the category, the ACR could be less than one        (theoretically ⅓ with fast playback and silence removal).    -   3. A significant number of messages, for which the ASR        performance is high, will require only a quick check and very        few keystrokes to make corrections, giving an ACR of about 2.    -   4. Most messages will still need significant editing. To what        extent these cases will benefit from the predictions still has        to be determined.    -   5. Handling capitalization and punctuation automatically should        reduce ACR by a small percentage and also improve consistency.

Questions/Issues

-   -   1. When does editing with ASR-controlled predictions become more        time consuming then simply typing? To take advantage of the        predictions, the agent needs to read them. If the next few words        are predicted correctly, simply accepting them should be faster        than typing them but if the next word is wrong, the additional        time required to check it is simply wasted. On the other hand,        the agent needs to listen anyway and might as well use the time        for checking the predictions.

Combining Prediction Methods

It seems promising to use statistical predictions as a back-up forASR-based prediction.

Since the statistical prediction model is static (not call dependent)and hence doesn't need to be transmitted to the QC application withevery message, it can be rather comprehensive. Lattices have to betransmitted for each message and hence will have to be kept withincertain size limits and are likely to miss some of the hypothesisneeded.

Both the statistical and the ASR-based prediction models would berepresented as graphs and the task of combining the predictions wouldinvolve traversing both graphs separately and then either choosing themore reliable prediction or combining them according to someinterpolation formula.

This method could be extended to more prediction model graphs, forinstance based on call pairs.

Statistical Predictions

These predictions are based on n-gram language models. These modelsstore conditional word sequence probabilities. For instance, a 4-grammodel might store the probability of the word “to” following the threeword context “I am going”. These models can be very big and efficientways of storing them are required that also enable fast generation ofpredictions.

Implementation

N-gram models are typically stored in a graph structure where each noderepresents a context (already transcribed words) and each outgoing linkis annotated with a word and the corresponding conditional probability.

Since there will always be words that were never (or very rarely)encountered after a certain context but that are still required at runtime, the model needs a way of dealing with previously unseen words in agiven context. This is achieved by “backing-off” to the correspondingshorter context. In our example, if “to” had not been observed after “Iam going”, the model would look for “to” after “am going”. If it wasn'tobserved there either, it would look at the context node for “going” andfinally at the empty context node where all the words in the vocabularyare represented. This “backing off” is implemented by adding a speciallink to each context node that points to the node with the correspondingshorter context and is annotated with a “back-off penalty” that can beinterpreted as the (logarithm of) the probability mass not distributedover all the other links outgoing from the node.

The overall log probability of “to” after “I am going” could forinstance be calculated as back_off(“I am going”)+back_off(“amgoing”)+link prob(“to” @ context node “going”).

Word Graph Expansion

It would be computationally expensive to search for the most likely word(link) starting with a given character sequence every time the userpresses a key. One way to speed this up relies on expanding the wordgraph into a character graph where the outgoing links at each node aresorted by decreasing likelihood. Note that the maximum number ofoutgoing links at each node is the number of characters in the languageplus two for the back-off link and the word end link. Hence searchingthrough this list would require at most about 100 character comparisonsfor English with the expected cost less than about 50 comparisons takinginto account that the most likely words will be tried first.

This ignores the cost of searching for words not found at the currentcontext node. When this is required, it might be best to accept thatpredictions can't be generated very quickly and to use the normalback-off link to search at the back-ff context nodes. The alternative ofstoring back-off links at each character node would require too muchmemory.

The expansion from the word graph to the character graph can beimplemented in the following way:

-   -   1. For each (word-level) context node:        -   sort all outgoing links by their probability (decreasing).        -   For each link (in order):            -   set pointer to current (word) node            -   for each character:                -   if there is already a link annotated with this                    character, set pointer to the node the link points                    to                -   else: add new link to the node the pointer points to                    and create a new node as destination for the link.                    Set pointer to this new node.            -   Add new link to pointer target, pointing to the                destination of the current word link.

After the expansion, all word links (including their probabilities) canbe deleted, except for the back-off links. Note that this will allow toalways find the most likely phrase prediction but not the list of lesslikely ones. If this is required (later), the sequence in which thecharacter expansion was performed would have to be stored in some way.

Prediction

Taking a word node id, a character node id, the current word sub-stringand a character as input, the “prediction” method would:

-   -   1. Goto character node[character_node_id]    -   2. Find link annotated with the input character (use linear        search in likelihood sorted links)    -   3. If found, follow the link and starting from its target node,        follow the first link leaving each node until some stop        condition is reached. At each transition, add the character        found at the link to the result string. Return the node id of        the initial target node and the result string.    -   4. Else: use back-off link from word_nodes[word_node_id] and the        current word string to find predictions at the back-off node(s).        This is not expected to find predictions at real-time if the        user types quickly.

Appendix III

Core Concepts

The following concepts are covered. Each Core Concept A—I can becombined with any other core concept in an implementation.

The following text also describes various sub-systems that, inter alia,implement features of the Core Concepts. These sub-systems need not beseparate from one another; for example, one sub-system may be part ofanother of the sub-systems. Nor do the sub-systems have to be discretein any other way; code implementing functions of one sub-system can formpart of the same software program as code implementing functions ofanother sub-system.

Core Concept A

A mass-scale, user-independent, device-independent, voice messagingsystem that converts unstructured voice messages into text for displayon a screen; the system comprising (i) computer implemented sub-systemsand also (ii) a network connection to human operators providingtranscription and quality control; the system being adapted to optimisethe effectiveness of the human operators by further comprising:

3 core sub-systems, namely (i) a pre-processing front end thatdetermines an appropriate conversion strategy; (ii) one or moreconversion resources; and (iii) a quality control sub-system.

Other features:

-   -   The conversion resources include one or more of the following:        one or more ASR engines; signal processing resources; the human        operators.        -   The signal processing resources optimise the quality of the            audio for conversion by performing one or more of the            following functions: removing noise, cleaning up known            defects, normalising volume/signal energy, removing            silent/empty sections.    -   Human operators perform random quality assurance testing on        converted messages and provide feedback to the pre-processing        front-end and/or the conversion resources.

Core Concept B

Context Vectors

A mass-scale, user-independent, device-independent, voice messagingsystem that converts unstructured voice messages into text for displayon a screen; the system comprising (i) computer implemented sub-systemsand also (ii) a network connection to human operators providingtranscription and quality control; the system being adapted to optimisethe effectiveness of the human operators by further comprising:

a computer implemented context sub-system adapted to use informationabout the context of a message or a part of a message to improve theconversion accuracy.

Other features:

-   -   the context information is used to limit the vocabulary used in        any ASR engine or refine search or matching processes used by        the ASR engine.    -   the context information is used to select a particular        conversion resource or combination of conversion resources, such        as a particular ASR engine.    -   context information includes one or more of caller ID, recipient        ID, whether the caller or recipient is a business or other type        of classifiable entity or not; caller-specific language;        call-pair history; time of call; day of call; geo-reference or        other location data of the caller or callee; PIM data (personal        information management data, including address book, diary) of        the caller or callee; the message type, including whether the        message is a voice mail, a spoken text, an instant message, a        blog entry, an e-mail, a memo, or a note; message length;        information discoverable using an online corpus of knowledge;        presence data; speech density of the message; speech quality of        the message.    -   the context sub-system includes a recogniser confidence        sub-system that determines automatically the confidence level        associated with a conversion of a specific message, or part of a        message, using the context information.    -   the context sub-system includes or is connected to a recogniser        confidence sub-system that determines automatically the        confidence level associated with a conversion of a specific        message, or part of a message, using the output of one or more        ASR engines.        -   the recogniser confidence sub-system can dynamically weight            how it uses the output of different ASR engines depending on            their likely effectiveness or accuracy.    -   knowledge of the context of a message is extracted by one        sub-system and fed-forward to a downstream sub-system that uses        that context information to improve conversion performance        -   a downstream sub-system is a quality monitoring and/or            assurance and control sub-system.

Core Concept C

Call-Pair History

A mass-scale, user-independent, device-independent, voice messagingsystem that converts unstructured voice messages into text for displayon a screen; the system comprising (i) computer implemented sub-systemsand also (ii) a network connection to human operators providingtranscription and quality control; the system being adapted to optimisethe effectiveness of the human operators by further comprising:

a computer implemented call-pair sub-system adapted to use call-pairhistory information to improve conversion accuracy.

Other features:

-   -   the call-pair history enables the system to be user-independent        but to acquire over time, without explicit user training,        user-dependent data that enables conversion performance to be        improved.    -   the call-pair history is associated with a pair of numbers,        including numbers associated with mobile telephones, fixed        telephones, IP addresses, e-mail addresses, or unique addresses        provided by a network.    -   The call-pair history includes information relating to one or        more of: likely language or dialect being used; country called        from or called to; time zones; time of call; day of call;        specific phrases used; caller-specific language; intonation; PIM        data (personal information management data, including address        book, diary).    -   a computer implemented dynamic language model sub-system adapted        to build a dynamic language model using one or more of: caller        dependence; call-pair dependence; callee dependence.        -   The caller is anyone depositing a voice message,            irrespective of whether they intend to place a voice call;            and the callee is anyone who reads the converted message,            irrespective of whether they were meant to receive a voice            call.    -   a computer implemented personal profile sub-system adapted to        build a personal profile of a caller to improve conversion        accuracy.        -   The personal profile includes words, phrases, grammar, or            intonation of the caller.

Core Concept D

3 Part Message Taxonomy

A mass-scale, user-independent, device-independent, voice messagingsystem that converts unstructured voice messages into text for displayon a screen; the system comprising (i) computer implemented sub-systemsand also (ii) a network connection to human operators providingtranscription and quality control; the system being adapted to optimisethe effectiveness of the human operators by further comprising:

a computer implemented boundary selection sub-system adapted to processa message by looking for the boundaries between sections of the messagewhich carry different types of content or carry different types ofmessage.

Other features:

-   -   the computer implemented boundary selection sub-system analyses        for one or more of the following component parts: a greeting        part; a body part; a goodbye part.        -   different conversion strategies are applied to each part,            the applied strategy being optimal for converting that part.        -   different parts of the message have different quality            requirements and a quality assessment sub-system applies            different standards to those different parts.        -   A speech quality estimator detects boundaries between            sections of the message which carry different types of            content or carry different types of message.            -   Boundaries are detected or inferred at regions in the                message where the speech density alters.            -   Boundaries are detected or inferred at a pause in the                message.            -   Boundaries are inferred as arising at a pre-defined                proportion of the message.                -   A greeting boundary is inferred at approximately 15%                    of the entire message length.

Core Concept E

Pre-Processing Front-End

A mass-scale, user-independent, device-independent, voice messagingsystem that converts unstructured voice messages into text for displayon a screen; the system comprising (i) computer implemented sub-systemsand also (ii) a network connection to human operators providingtranscription and quality control; the system being adapted to optimisethe effectiveness of the human operators by further comprising:

a computer implemented pre-processing front-end sub-system thatdetermines an appropriate conversion strategy used to convert the voicemessages.

Other features:

-   -   The pre-processing front end optimises the quality of the audio        for conversion by performing one or more of the following        functions: removing noise, cleaning up known defects,        normalising volume/signal energy, removing silent/empty sections        and classifies the message type for optimally routing the        message for conversion, or not.    -   The pre-processing front-end determines the language being used        by the caller, based on one or more of the following: knowledge        about registration, location and call history of caller and/or        receiver.    -   The pre-processing front-end selects a particular ASR engine to        convert a message or part of a message.        -   different conversion resources, such as ASR engines, are            used for different parts of the same message.        -   different conversion resources, such as ASR engines, are            used for different messages.        -   the human operators are treated as ASR engines.        -   the pre-processing front-end uses or is connected with a            recogniser confidence sub-system to determine automatically            the confidence level associated with a conversion of a            specific message, or part of a message, and a particular            conversion resource, such as an ASR engine, is then deployed            depending on that confidence level.    -   the conversion strategy involves the selection a conversion        strategy from a set of conversion strategies that include the        following: (i) messages for which an ASR conversion confidence        is sufficiently high are checked automatically by a quality        assessment sub-system for conformance to quality standards; (ii)        messages for which the ASR conversion confidence is not        sufficiently high are routed to a human operator for checking        and, if necessary, correction; (iii) messages for which the ASR        conversion confidence is very low are flagged as unconvertible        and the user is informed of the receipt of an unconvertible        message.

Core Concept F

Queue Manager

A mass-scale, user-independent, device-independent, voice messagingsystem that converts unstructured voice messages into text for displayon a screen; the system comprising (i) computer implemented sub-systemsand also (ii) a network connection to human operators providingtranscription and quality control; the system being adapted to optimisethe effectiveness of the human operators by further comprising:

a computer implemented queue manager sub-system that intelligentlymanages loading and calls in resources as required to ensure thatconverted message delivery times meet a pre-defined standard.

Other features:

-   -   The queue manager sub-system determines what should happen to a        voice message at each stage of processing through the system.        -   If at any stage of the automated conversion, confidence            intervals or other measures suggest any part of a message            are not good enough, then the queue manager directs this to            the correct human operator for assistance.    -   The queue manager sub-system makes decisions by calculating        trade-offs between conversion time and quality.    -   The queue manager sub-system uses state-machines that, for any        given language queue, can decide how best to process the        messages through the system.

Core Concept G

Lattice

A mass-scale, user-independent, device-independent, voice messagingsystem that converts unstructured voice messages into text for displayon a screen; the system comprising (i) computer implemented sub-systemsand also (ii) a network connection to human operators providingtranscription and quality control; the system being adapted to optimisethe effectiveness of the human operators by further comprising

a computer implemented lattice sub-system that generates a lattice ofpossible word or phrase sequences and enables a human operator to guidea conversion sub-system by being shown one or more candidate convertedwords or phrases from the lattice and enabling the operator to eitherselect that candidate word or phrase or, by entering one or morecharacters for a different converted word or phrase, to trigger theconversion sub-system to propose an alternative word or phrase.

Other features:

-   -   The conversion sub-system receives inputs from a sub-system that        handles call-pair history information.    -   The conversion sub-system receives inputs from conversion        resources.    -   The conversion sub-system receives inputs from a context        sub-system that has knowledge of the context of a message.    -   The conversion sub-system learns, from the human operator        inputs, likely words corresponding to a sound pattern.    -   The human operator is required to select only a single key to        accept a word or phrase.    -   The conversion sub-system automatically provides capitalisation        and punctuation.    -   The conversion sub-system can propose candidate numbers, real        nouns, web addresses, e-mail addresses, physical addresses,        location information, or other coordinates.    -   The conversion sub-system automatically differentiates between        parts of the message that are likely to be important and those        that are likely to be unimportant.    -   Unimportant parts of the message are confirmed by the operator        as belonging to a class proposed by the conversion sub-system        and are then converted solely by a machine ASR engine.    -   The human operator can speak the correct word to the conversion        system, which then automatically transcribes it.

Core Concept H

On-Line Corpus

A mass-scale, user-independent, device-independent, voice messagingsystem that converts unstructured voice messages into text for displayon a screen; the system comprising (i) computer implemented sub-systemsand also (ii) a network connection to human operators providingtranscription and quality control; the system being adapted to optimisethe effectiveness of the human operators by further comprising:

a computer implemented search sub-system that analyses a convertedmessage against an on-line corpus of knowledge.

Other features:

-   -   The on-line corpus of knowledge is the internet, as accessed by        a search engine.    -   The on-line corpus of knowledge is a search engine database,        such as Google.    -   The analysis of the converted message enables the accuracy of        the conversion to be assessed by a human operator an/or a        recogniser confidence sub-system.    -   The analysis of the converted message enables ambiguities in the        message to be resolved by a human operator and/or an ASR engine.

Core Concept I

Detectors

A mass-scale, user-independent, device-independent, voice messagingsystem that converts unstructured voice messages into text for displayon a screen; the system comprising (i) computer implemented sub-systemsand also (ii) a network connection to human operators providingtranscription and quality control; the system being adapted to optimisethe effectiveness of the human operators by further comprising:

a computer implemented detector sub-system that is adapted to detectslam-downs.

Other features:

-   -   The slam down detector is implemented as part of a        pre-processing front-end.

Other Detectors That Can Also Be Used:

-   -   A computer implemented detector sub-system that is tuned to        detect different spoken languages, such as English, Spanish,        French etc.        -   The language detector can detect changes in language part            way through a message.        -   The language detector can use inputs from a sub-system that            has call-pair history information that records how changes            in language occurred in earlier messages.    -   A computer implemented detector sub-system that is adapted to        estimate speech quality        -   The speech quality estimator finds drop-outs, estimate noise            levels and calculate an overall measure of voice quality and            uses an adaptive threshold to reject lowest quality            messages.    -   A computer implemented detector sub-system that is adapted to        detect slam-downs.        -   The slam down detector is implemented as part of a            pre-processing front-end.    -   A computer implemented detector sub-system that is adapted to        detect inadvertent calls.        -   The inadvertent call detector is implemented as part of a            pre-processing front-end.    -   A computer implemented detector sub-system that is adapted to        detect and convert pre-recorded messages.    -   A computer implemented detector sub-system that is adapted to        detect and convert spoken numbers.    -   A computer implemented detector sub-system that is adapted to        detect and convert spoken addresses.    -   A computer implemented detector sub-system that is adapted to        detect and convert real nouns, numbers, web addresses, e-mail        addresses, physical addresses, location information, other        coordinates.

Message Types

-   -   The message is a voicemail intended for a mobile telephone and        the voice message is converted to text and sent to that mobile        telephone.    -   The message is a voice message intended for an instant messaging        service and the voice message is converted to text and sent to        an instant messaging service for a display on a screen.    -   The message is a voice message intended for a web blog and the        voice message is converted to text and sent to a server for        display as part of the web blog.    -   The message is a voice message intended to be converted to text        format and sent as a text message.    -   The message is a voice message intended to be converted to text        format and sent as an email message.    -   The message is a voice message intended to be converted to text        format and sent as a note or memo, by email or text, to an        originator of the message.

Other Elements of the Value Chain

-   -   A mobile telephone network that is connected to the system of        any preceding Claim.    -   A mobile telephone when displaying a message converted by the        system of any preceding Claim.    -   A computer display screen when displaying a message converted by        the system of any preceding Claim.    -   A method of providing voice messaging, comprising the step of a        user sending a voice message to a messaging system as claimed in        any preceding Claim.

1. A mass-scale, user-independent, device-independent, voice messagingsystem that converts an unstructured voice message into text for displayon a screen; the system comprising (i) computer implemented sub-systemsand also (ii) a network connection to human operators providingtranscription and quality control; the system being adapted to optimisethe effectiveness of the human operators by further comprising: acomputer implemented queue manager sub-system that uses historic data tointelligently manage loading and calling in of resources as required toensure that converted message delivery times meet a pre-definedstandard.
 2. The system of claim 1 in which the historic data has beenanalysed to determine optimal decision making.
 3. The system of claim 1in which the historic data has been analysed to determine optimaltrade-offs.